Effective classification of 2DPCA and 2DLDA features for face recognition

In order to enhance the classification accuracy of the two-dimensional feature of the image, the idea of a separate classification for each projection direction feature is proposed in this paper. Our method first divides the image into blocks and finds the two-dimensional sub-projection matrix of each sub-block, and then completes the feature extraction by using each column of the projection matrix. At the stage of feature classification, the feature of each projection direction of each sub-block is classified separately, cast a vote towards images classification and the result with the maximum votes wins. We named this method decision level vote classification (DL_VC). The proposed method is equivalent to transforming global features into local features, and the voting method can reduce the influence of local interference. Experiments on face databases demonstrate the efficacy of the proposed method.